{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai import *\n",
    "from fastai.collab import *\n",
    "from fastai.tabular import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collaborative filtering example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`collab` models use data in a `DataFrame` of user, items, and ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "user,item,title = 'userId','movieId','title'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('/home/jhoward/.fastai/data/movie_lens_sample')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = untar_data(URLs.ML_SAMPLE)\n",
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73</td>\n",
       "      <td>1097</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1255504951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>561</td>\n",
       "      <td>924</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1172695223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>157</td>\n",
       "      <td>260</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1291598691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>358</td>\n",
       "      <td>1210</td>\n",
       "      <td>5.0</td>\n",
       "      <td>957481884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>130</td>\n",
       "      <td>316</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1138999234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating   timestamp\n",
       "0      73     1097     4.0  1255504951\n",
       "1     561      924     3.5  1172695223\n",
       "2     157      260     3.5  1291598691\n",
       "3     358     1210     5.0   957481884\n",
       "4     130      316     2.0  1138999234"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv(path/'ratings.csv')\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's all we need to create and train a model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = CollabDataBunch.from_df(ratings, seed=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_range = [0,5.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = collab_learner(data, n_factors=50, y_range=y_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 00:04\n",
      "epoch  train_loss  valid_loss\n",
      "1      1.600185    0.962681    (00:01)\n",
      "2      0.851333    0.678732    (00:01)\n",
      "3      0.660136    0.666290    (00:01)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit_one_cycle(3, 5e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Movielens 100k"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try with the full Movielens 100k data dataset, available from http://files.grouplens.org/datasets/movielens/ml-100k.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path=Path('data/ml-100k/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>377</td>\n",
       "      <td>1</td>\n",
       "      <td>878887116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>880606923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>886397596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp\n",
       "0     196      242       3  881250949\n",
       "1     186      302       3  891717742\n",
       "2      22      377       1  878887116\n",
       "3     244       51       2  880606923\n",
       "4     166      346       1  886397596"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv(path/'u.data', delimiter='\\t', header=None,\n",
    "                      names=[user,item,'rating','timestamp'])\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>date</th>\n",
       "      <th>N</th>\n",
       "      <th>url</th>\n",
       "      <th>g0</th>\n",
       "      <th>g1</th>\n",
       "      <th>g2</th>\n",
       "      <th>g3</th>\n",
       "      <th>g4</th>\n",
       "      <th>...</th>\n",
       "      <th>g9</th>\n",
       "      <th>g10</th>\n",
       "      <th>g11</th>\n",
       "      <th>g12</th>\n",
       "      <th>g13</th>\n",
       "      <th>g14</th>\n",
       "      <th>g15</th>\n",
       "      <th>g16</th>\n",
       "      <th>g17</th>\n",
       "      <th>g18</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?GoldenEye%20(...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Four%20Rooms%...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Get%20Shorty%...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "      <td>01-Jan-1995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://us.imdb.com/M/title-exact?Copycat%20(1995)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   movieId              title         date   N  \\\n",
       "0        1   Toy Story (1995)  01-Jan-1995 NaN   \n",
       "1        2   GoldenEye (1995)  01-Jan-1995 NaN   \n",
       "2        3  Four Rooms (1995)  01-Jan-1995 NaN   \n",
       "3        4  Get Shorty (1995)  01-Jan-1995 NaN   \n",
       "4        5     Copycat (1995)  01-Jan-1995 NaN   \n",
       "\n",
       "                                                 url  g0  g1  g2  g3  g4 ...   \\\n",
       "0  http://us.imdb.com/M/title-exact?Toy%20Story%2...   0   0   0   1   1 ...    \n",
       "1  http://us.imdb.com/M/title-exact?GoldenEye%20(...   0   1   1   0   0 ...    \n",
       "2  http://us.imdb.com/M/title-exact?Four%20Rooms%...   0   0   0   0   0 ...    \n",
       "3  http://us.imdb.com/M/title-exact?Get%20Shorty%...   0   1   0   0   0 ...    \n",
       "4  http://us.imdb.com/M/title-exact?Copycat%20(1995)   0   0   0   0   0 ...    \n",
       "\n",
       "   g9  g10  g11  g12  g13  g14  g15  g16  g17  g18  \n",
       "0   0    0    0    0    0    0    0    0    0    0  \n",
       "1   0    0    0    0    0    0    0    1    0    0  \n",
       "2   0    0    0    0    0    0    0    1    0    0  \n",
       "3   0    0    0    0    0    0    0    0    0    0  \n",
       "4   0    0    0    0    0    0    0    1    0    0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = pd.read_csv(path/'u.item',  delimiter='|', encoding='latin-1', header=None,\n",
    "                    names=[item, 'title', 'date', 'N', 'url', *[f'g{i}' for i in range(19)]])\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>875747190</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>226</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>883888671</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>154</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>879138235</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>306</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>876503793</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp         title\n",
       "0     196      242       3  881250949  Kolya (1996)\n",
       "1      63      242       3  875747190  Kolya (1996)\n",
       "2     226      242       5  883888671  Kolya (1996)\n",
       "3     154      242       3  879138235  Kolya (1996)\n",
       "4     306      242       5  876503793  Kolya (1996)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_movie = ratings.merge(movies[[item, title]])\n",
    "rating_movie.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = CollabDataBunch.from_df(rating_movie, seed=42, pct_val=0.1, item_name=title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>  <col width='10px'>  <col width='10px'>  <col width='10px'>  <tr>\n",
       "    <th>userId</th>\n",
       "    <th>title</th>\n",
       "    <th>target</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>588</th>\n",
       "    <th>Twister (1996)</th>\n",
       "    <th>3.0</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>664</th>\n",
       "    <th>Grifters, The (1990)</th>\n",
       "    <th>4.0</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>758</th>\n",
       "    <th>Wings of the Dove, The (1997)</th>\n",
       "    <th>4.0</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>711</th>\n",
       "    <th>Empire Strikes Back, The (1980)</th>\n",
       "    <th>5.0</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>610</th>\n",
       "    <th>People vs. Larry Flynt, The (1996)</th>\n",
       "    <th>3.0</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>407</th>\n",
       "    <th>Star Trek: The Wrath of Khan (1982)</th>\n",
       "    <th>4.0</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>649</th>\n",
       "    <th>Independence Day (ID4) (1996)</th>\n",
       "    <th>2.0</th>\n",
       "  </tr>\n",
       "  <tr>\n",
       "    <th>798</th>\n",
       "    <th>Sabrina (1954)</th>\n",
       "    <th>4.0</th>\n",
       "  </tr>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.show_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_range = [0,5.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = collab_learner(data, n_factors=40, y_range=y_range, wd=1e-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.lr_find()\n",
    "learn.recorder.plot(skip_end=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total time: 00:33\n",
      "epoch  train_loss  valid_loss\n",
      "1      0.938132    0.928146    (00:06)\n",
      "2      0.862458    0.885790    (00:06)\n",
      "3      0.753191    0.831451    (00:06)\n",
      "4      0.667046    0.814966    (00:07)\n",
      "5      0.546363    0.813588    (00:06)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "learn.fit_one_cycle(5, 5e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('dotprod')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's [some benchmarks](https://www.librec.net/release/v1.3/example.html) on the same dataset for the popular Librec system for collaborative filtering. They show best results based on RMSE of 0.91, which corresponds to an MSE of `0.91**2 = 0.83`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpretation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.load('dotprod');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmbeddingDotBias(\n",
       "  (u_weight): Embedding(944, 40)\n",
       "  (i_weight): Embedding(1654, 40)\n",
       "  (u_bias): Embedding(944, 1)\n",
       "  (i_bias): Embedding(1654, 1)\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Star Wars (1977)', 'Contact (1997)', 'Fargo (1996)', 'Return of the Jedi (1983)', 'Liar Liar (1997)',\n",
       "       'English Patient, The (1996)', 'Scream (1996)', 'Toy Story (1995)', 'Air Force One (1997)',\n",
       "       'Independence Day (ID4) (1996)'], dtype=object)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = rating_movie.groupby(title)['rating'].count()\n",
    "top_movies = g.sort_values(ascending=False).index.values[:1000]\n",
    "top_movies[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Movie bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1000])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_bias = learn.bias(top_movies, is_item=True)\n",
    "movie_bias.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_ratings = rating_movie.groupby(title)['rating'].mean()\n",
    "movie_ratings = [(b, i, mean_ratings.loc[i]) for i,b in zip(top_movies,movie_bias)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "item0 = lambda o:o[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor(-0.3264),\n",
       "  'Children of the Corn: The Gathering (1996)',\n",
       "  1.3157894736842106),\n",
       " (tensor(-0.3241),\n",
       "  'Lawnmower Man 2: Beyond Cyberspace (1996)',\n",
       "  1.7142857142857142),\n",
       " (tensor(-0.2799), 'Island of Dr. Moreau, The (1996)', 2.1578947368421053),\n",
       " (tensor(-0.2761), 'Mortal Kombat: Annihilation (1997)', 1.9534883720930232),\n",
       " (tensor(-0.2703), 'Cable Guy, The (1996)', 2.339622641509434),\n",
       " (tensor(-0.2484), 'Leave It to Beaver (1997)', 1.8409090909090908),\n",
       " (tensor(-0.2413), 'Crow: City of Angels, The (1996)', 1.9487179487179487),\n",
       " (tensor(-0.2395), 'Striptease (1996)', 2.2388059701492535),\n",
       " (tensor(-0.2389), 'Free Willy 3: The Rescue (1997)', 1.7407407407407407),\n",
       " (tensor(-0.2346), 'Barb Wire (1996)', 1.9333333333333333),\n",
       " (tensor(-0.2325), 'Grease 2 (1982)', 2.0),\n",
       " (tensor(-0.2294), 'Beverly Hills Ninja (1997)', 2.3125),\n",
       " (tensor(-0.2223), \"Joe's Apartment (1996)\", 2.2444444444444445),\n",
       " (tensor(-0.2218), 'Bio-Dome (1996)', 1.903225806451613),\n",
       " (tensor(-0.2117), \"Stephen King's The Langoliers (1995)\", 2.413793103448276)]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_ratings, key=item0)[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor(0.6105), \"Schindler's List (1993)\", 4.466442953020135),\n",
       " (tensor(0.5817), 'Titanic (1997)', 4.2457142857142856),\n",
       " (tensor(0.5685), 'Shawshank Redemption, The (1994)', 4.445229681978798),\n",
       " (tensor(0.5451), 'L.A. Confidential (1997)', 4.161616161616162),\n",
       " (tensor(0.5350), 'Rear Window (1954)', 4.3875598086124405),\n",
       " (tensor(0.5341), 'Silence of the Lambs, The (1991)', 4.28974358974359),\n",
       " (tensor(0.5330), 'Star Wars (1977)', 4.3584905660377355),\n",
       " (tensor(0.5227), 'Good Will Hunting (1997)', 4.262626262626263),\n",
       " (tensor(0.5114), 'As Good As It Gets (1997)', 4.196428571428571),\n",
       " (tensor(0.4800), 'Casablanca (1942)', 4.45679012345679),\n",
       " (tensor(0.4698), 'Boot, Das (1981)', 4.203980099502488),\n",
       " (tensor(0.4589), 'Close Shave, A (1995)', 4.491071428571429),\n",
       " (tensor(0.4567), 'Apt Pupil (1998)', 4.1),\n",
       " (tensor(0.4566), 'Vertigo (1958)', 4.251396648044692),\n",
       " (tensor(0.4542), 'Godfather, The (1972)', 4.283292978208232)]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_ratings, key=lambda o: o[0], reverse=True)[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Movie weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1000, 40])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_w = learn.weight(top_movies, is_item=True)\n",
    "movie_w.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1000, 3])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_pca = movie_w.pca(3)\n",
    "movie_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fac0,fac1,fac2 = movie_pca.t()\n",
    "movie_comp = [(f, i) for f,i in zip(fac0, top_movies)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor(1.0834), 'Chinatown (1974)'),\n",
       " (tensor(1.0517), 'Wrong Trousers, The (1993)'),\n",
       " (tensor(1.0271), 'Casablanca (1942)'),\n",
       " (tensor(1.0193), 'Close Shave, A (1995)'),\n",
       " (tensor(1.0093), 'Secrets & Lies (1996)'),\n",
       " (tensor(0.9771), 'Lawrence of Arabia (1962)'),\n",
       " (tensor(0.9724), '12 Angry Men (1957)'),\n",
       " (tensor(0.9660), 'Some Folks Call It a Sling Blade (1993)'),\n",
       " (tensor(0.9517), 'Ran (1985)'),\n",
       " (tensor(0.9460), 'Third Man, The (1949)')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor(-1.2521), 'Jungle2Jungle (1997)'),\n",
       " (tensor(-1.1917), 'Children of the Corn: The Gathering (1996)'),\n",
       " (tensor(-1.1746), 'Home Alone 3 (1997)'),\n",
       " (tensor(-1.1325), \"McHale's Navy (1997)\"),\n",
       " (tensor(-1.1266), 'Bio-Dome (1996)'),\n",
       " (tensor(-1.1115), 'D3: The Mighty Ducks (1996)'),\n",
       " (tensor(-1.1062), 'Leave It to Beaver (1997)'),\n",
       " (tensor(-1.1051), 'Congo (1995)'),\n",
       " (tensor(-1.0934), 'Batman & Robin (1997)'),\n",
       " (tensor(-1.0904), 'Flipper (1996)')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_comp = [(f, i) for f,i in zip(fac1, top_movies)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor(0.8120), 'Ready to Wear (Pret-A-Porter) (1994)'),\n",
       " (tensor(0.7939), 'Keys to Tulsa (1997)'),\n",
       " (tensor(0.7862), 'Nosferatu (Nosferatu, eine Symphonie des Grauens) (1922)'),\n",
       " (tensor(0.7634), 'Trainspotting (1996)'),\n",
       " (tensor(0.7494), 'Brazil (1985)'),\n",
       " (tensor(0.7492), 'Heavenly Creatures (1994)'),\n",
       " (tensor(0.7446), 'Clockwork Orange, A (1971)'),\n",
       " (tensor(0.7420), 'Beavis and Butt-head Do America (1996)'),\n",
       " (tensor(0.7271), 'Rosencrantz and Guildenstern Are Dead (1990)'),\n",
       " (tensor(0.7249), 'Jude (1996)')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(tensor(-1.1900), 'Braveheart (1995)'),\n",
       " (tensor(-1.0113), 'Raiders of the Lost Ark (1981)'),\n",
       " (tensor(-0.9670), 'Titanic (1997)'),\n",
       " (tensor(-0.9409), 'Forrest Gump (1994)'),\n",
       " (tensor(-0.9151), \"It's a Wonderful Life (1946)\"),\n",
       " (tensor(-0.8721), 'American President, The (1995)'),\n",
       " (tensor(-0.8211), 'Top Gun (1986)'),\n",
       " (tensor(-0.8207), 'Hunt for Red October, The (1990)'),\n",
       " (tensor(-0.8177), 'Sleepless in Seattle (1993)'),\n",
       " (tensor(-0.8114), 'Pretty Woman (1990)')]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0))[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "idxs = np.random.choice(len(top_movies), 50, replace=False)\n",
    "idxs = list(range(50))\n",
    "X = fac0[idxs]\n",
    "Y = fac2[idxs]\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.scatter(X, Y)\n",
    "for i, x, y in zip(top_movies[idxs], X, Y):\n",
    "    plt.text(x,y,i, color=np.random.rand(3)*0.7, fontsize=11)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
